Quantitative identification method of gas kick in deep-water drilling

During the process of oil and gas exploration in deep-water drilling, the gas kick phenomenon leads to a decrease in drilling fluid density, thereby increasing the risk of a blowout. However, since the gas kick process is a complex system with characteristics such as chaos, dissipation, and pseudo-randomness, it increases the difficulty of quantitative identification of gas content. To this end, this research proposes an acoustic quantitative identification algorithm for liquid-phase gas content based on the fusion of complementary ensemble empirical mode decomposition (CEEMD) and dynamic spectral centroid (DSC). This method effectively suppresses background noise and extracts feature signals reflecting the movement state of bubbles by decomposing the received ultrasonic signals into several intrinsic mode functions. Meanwhile, by introducing the DSC algorithm to quantify the energy distribution state of the extracted feature signals in the frequency domain, a mapping relationship between ultrasonic Doppler frequency shift and gas content can be effectively established. Meanwhile, to compensate for the prediction bias of the aforementioned algorithm regarding gas content in non-Newtonian fluid systems, this research, based on the Buckingham π theorem and bubble force balance analysis, derived and constructed a bubble equivalent diameter prediction model and a drag coefficient characterization model that account for the rheological characteristics of non-Newtonian fluids. Experimental results show that the identification accuracy error of the CEEMD-DSC-based gas kick quantitative identification algorithm for gas content under different operating conditions is controlled within ±20%, thereby verifying the effectiveness and reliability of the established algorithm.